Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing
Consumption of fish as a food requirement for the fulfillment of community nutrition is increasing. This was followed by an increase in the amount of fish caught that were sold at fish markets. Market managers must be concerned about the dispersion of huge amounts of fish in the market in order to d...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ikatan Ahli Informatika Indonesia
2022-06-01
|
| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4062 |
| _version_ | 1797334641672192000 |
|---|---|
| author | Sabarudin Saputra Anton Yudhana Rusydi Umar |
| author_facet | Sabarudin Saputra Anton Yudhana Rusydi Umar |
| author_sort | Sabarudin Saputra |
| collection | DOAJ |
| description | Consumption of fish as a food requirement for the fulfillment of community nutrition is increasing. This was followed by an increase in the amount of fish caught that were sold at fish markets. Market managers must be concerned about the dispersion of huge amounts of fish in the market in order to determine the freshness of the fish before it reaches the hands of consumers. So far, market managers have relied on traditional ways to determine the freshness of fish in circulation. The issue is that traditional solutions, such as the use expert assessment, demand a human physique that quickly experiences fatigue. Technological developments can be a solution to these problems, such as utilizing image processing techniques classification method. Image processing with the use of color features is an effective method to determine the freshness of fish. The classification method used in this research is the Naive Bayes method. This study aims to identify the freshness of fish based on digital images and determine the performance level of the method. The identification process uses the RGB color value feature of fisheye images. The stages of fish freshness identification include cropping, segmentation, RGB value extraction, training, and testing. The classification data are 210 RGB value of extraction images which are divided into 147 data for training and 63 data for testing. The research data were divided into fresh class, started to rot class, and rotted class. The research shows that the Naive Bayes algorithm can be used in the process of identifying the freshness level of fish based on fisheye images with a test accuracy rate of 79.37%. |
| first_indexed | 2024-03-08T08:25:01Z |
| format | Article |
| id | doaj.art-fe18619598374803939499a300b4d8be |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T08:25:01Z |
| publishDate | 2022-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-fe18619598374803939499a300b4d8be2024-02-02T05:04:43ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-06-016341242010.29207/resti.v6i3.40624062Implementation of Naïve Bayes for Fish Freshness Identification Based on Image ProcessingSabarudin Saputra0Anton Yudhana1Rusydi Umar2Universitas Ahmad DahlanUniversitas Ahmad DahlanUniversitas Ahmad DahlanConsumption of fish as a food requirement for the fulfillment of community nutrition is increasing. This was followed by an increase in the amount of fish caught that were sold at fish markets. Market managers must be concerned about the dispersion of huge amounts of fish in the market in order to determine the freshness of the fish before it reaches the hands of consumers. So far, market managers have relied on traditional ways to determine the freshness of fish in circulation. The issue is that traditional solutions, such as the use expert assessment, demand a human physique that quickly experiences fatigue. Technological developments can be a solution to these problems, such as utilizing image processing techniques classification method. Image processing with the use of color features is an effective method to determine the freshness of fish. The classification method used in this research is the Naive Bayes method. This study aims to identify the freshness of fish based on digital images and determine the performance level of the method. The identification process uses the RGB color value feature of fisheye images. The stages of fish freshness identification include cropping, segmentation, RGB value extraction, training, and testing. The classification data are 210 RGB value of extraction images which are divided into 147 data for training and 63 data for testing. The research data were divided into fresh class, started to rot class, and rotted class. The research shows that the Naive Bayes algorithm can be used in the process of identifying the freshness level of fish based on fisheye images with a test accuracy rate of 79.37%.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4062identificationimagefisheyenaive bayesrgb |
| spellingShingle | Sabarudin Saputra Anton Yudhana Rusydi Umar Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) identification image fisheye naive bayes rgb |
| title | Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing |
| title_full | Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing |
| title_fullStr | Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing |
| title_full_unstemmed | Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing |
| title_short | Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing |
| title_sort | implementation of naive bayes for fish freshness identification based on image processing |
| topic | identification image fisheye naive bayes rgb |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4062 |
| work_keys_str_mv | AT sabarudinsaputra implementationofnaivebayesforfishfreshnessidentificationbasedonimageprocessing AT antonyudhana implementationofnaivebayesforfishfreshnessidentificationbasedonimageprocessing AT rusydiumar implementationofnaivebayesforfishfreshnessidentificationbasedonimageprocessing |